Factors that influence the acceptance of M-Learning Services in the institutes of higher education in Malaysia

Scientific Essay, 2015
8 Pages



M-learning is a new education channel that universally assists people in acquiring knowledge and skill via the use of mobile technologies. This study attempts to create a theoretical model, in which student acceptance of Mobile learning implementation in the three Islamic universities in Malaysia are explained and predicted. The model expands the belief concept in Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) by including one more constructs namely, service quality.


The success or failure of mobile learning services has so far been mainly investigated post trial and thus, it is necessary to investigate the factors, limitations and requirements that impact students’ acceptance of mobile learning prior to implementing the system. This is to ensure that the resources devoted to the implementation will serve their purposes: use and acceptance. Apart from that, as indicated by Embi et al. (2013) and Almatari et al. (2011) the universities could also benefit from the investigation in a sense that the findings of the investigation could aid the universities to strategically plan based on the students’ demands. As such, the universities could make better decision in their technology investment. Williams (2009) stressed the importance of having the knowledge on the influencing factors of m-learning services’ acceptance among students of higher education institutions since m-learning is a crucial alternative platform of learning. Further, individual’s subjective willingness and cognitive engagement in m-learning activities entails one of m-learning’s success factors. In order to enhance the availability of education in Malaysia, Alzaza and Yaakob (2011) have suggested that the higher education should engage in m-learning services. By doing so, Malaysia would be able to meet the priority of its strategy to brand the education particularly, the higher education (Robertson, 2008).


In order to assist in the development of a robust theoretical foundation, two well-established models of adoption and intention which are the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT) are utilised in this study; both TAM and IDT will be reviewed in the next subsections.

A. Technology Acceptance Model (TAM)

TAM consisted of five components namely, perceived ease of use (PEOU), perceived usefulness (PU), attitude toward using (ATU), behavioural intention to use (BI), and behaviour system use. Specifically, as indicated by Fred D. Davis (1989) PEOU represents the degree to which a user believes that using a particular service would be effortless, while PU means the degree to which an individual perceives that using a particular system would improve the performance of his or her job. PEOU and PU are the two most important factors for system use and in fact, according to Liu and Han (2010) these two elements (PEOU and PU) are the key beliefs which lead to user acceptance of information technology. Meanwhile, ATU directly predicts BI of the users, which determines AU. Later, an extension of TAM or known as TAM2 was proposed by Venkatesh and Davis (2000).

B. Innovation Diffusion Theory (IDT)

Innovation Diffusion Theory or IDT was proposed by Rogers (1962, 1983, 1995, 2003), and like the TAM, IDT is also a well-established theory for user adoption. IDT describes the process of innovation decision, the factors of adoption rate, and numerous categories of adopters, while innovation diffusion is attained through users’ acceptance and use of new ideas or things (Zaltman & Stiff, 1973). (Need to insert an introductory sentence/paragraph on relative advantage, compatibility, complexity, trialibility and observability here so that there is connection and flow. Maybe something on how these elements are related to/crucial in innovation decision etc.). Relative advantage is the extent to which an innovation is viewed as being superior to the idea it replaces, while compatibility is the extent to which an innovation is viewed as consistent with the present values, previous experiences, and needs of prospective adopters. Meanwhile, complexity is described as the extent to which an innovation is viewed as somewhat challenging to comprehend and utilise. On the other hand, triability refers to the extent to which an innovation may be tested on a restricted basis; implementing an innovation on a small scale basis will make full-scale adoption easier. Meanwhile, observability refers to the extent to which the outcomes of an innovation can be perceived by others; higher visibility leads to higher adoption rate.


This two theories have been proven to be highly successful in empirical studies such as indicated in studies by Igbaria, et al (1995), Igbaria, et al (1997), Karahanna, et al (1999), G.C. Moore and Benbasat (1996) and S. Taylor and P. Todd (1995) and as such, this study has chosen these theories as the base theories. TAM and IDT have the full capacity to study the Electronic Commerce (EC) and Internet application adoption, and at the same time, these two theories provide strong theoretical foundation for this study. Additionally, even though TAM and IDT originated from different fields, they share some noticeable resemblances. For instance, as highlighted by Moore and Benbasat (1991) and Wu and Wang (2005), the construct of relative advantage in IDT is often perceived as the equal to the PU construct in TAM, while the construct of complexity in IDT is very similar to PEOU concept in TAM. Further, some studies even combined TAM and IDT. For instance, in their study to evaluate and explain consumer behaviour in the context of virtual store, Chen, et al (2004) combined the original TAM with the compatibility contract, while Wu and Wang (2005) in their study has combined TAM2 with IDT. Figure A illustrates the building blocks for student acceptance of m-learning services.

illustration not visible in this excerpt

Figure A: Base Model for Student Acceptance of m-learning.


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Factors that influence the acceptance of M-Learning Services in the institutes of higher education in Malaysia
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Mohammed Al-zoubi et al. (Author), 2015, Factors that influence the acceptance of M-Learning Services in the institutes of higher education in Malaysia, Munich, GRIN Verlag, https://www.grin.com/document/287714


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